fMRI experiments produce large, numerically rich, but noisy data sets that pose a challenge for extracting the signal variance and establishing the correspondence between that signal and cognitive variables. Conventional analysis has reduced the dimensionality of fMRI data by searching for clusters of voxels that show similar responses to experimental manipulations and averaging the signal within those clusters. We have introduced a new approach to fMRI data analysis, "multi-voxel pattern analysis", that examines higher spatial frequency patterns of activity - the voxel-by-voxel variation of response within a region - and have shown that this method greatly increases the sensitivity of fMRI (Haxby et al. 2001;Hanson et al. 2004;OToole et al. 2004;Polyn et al. 2004). In the proposed investigations, we will develop new methods for analysis of spatially-distributed patterns of neural activity in relation to two specific problems in fMRI data analysis: 1. accounting for inter-individual variation in functional neuroanatomy, and 2. the relation between spatially-distributed neural population responses and cognitive representations. This work will involve the efforts of a multidisciplinary team consisting of cognitive neuroscientists, applied mathematicians, and signal- processing engineers. We propose the development of analytic methods for aligning the functional neuroanatomy of individual brains based on the patterns of neural activity that are elicited by a broad spectrum of cognitive activities. We predict that these methods will enhance the sensitivity of group statistical tests of fMRI data, will allow the investigation of the inter-individual consistency of higher spatial frequency topographic representations, and will provide explicit measures of inter-individual variation in the location, organization, and spatial extent of functional maps, with potential applications for studies of clinical conditions. We propose, further, to develop methods for detecting and analyzing distributed patterns of neural activity that make use of prior knowledge about the structure of the cognitive representations that are associated with those neural activities. We predict that these methods will increase the sensitivity of multi-voxel pattern analysis and will allow the investigation of how cognitive information is represented in topographically-organized, spatially-distributed patterns of neural activity.